Title:
A Self-Adjusting Approach for Temporal Dropout Prediction of E-Learning Students
Author:
Clauirton Albuquerque Siebra (Federal University of Paraiba, Paraiba, Brazil), Ramon N. Santos (Federal University of Paraiba, Paraiba, Brazil) & Natasha C.Q. Lino (Federal University of Paraiba, Paraiba, Brazil)
Published:
International Journal of Distance Education Technologies (IJDET), 18(2), 2020
Available: https://www.igi-global.com/article/a-self-adjusting-approach-for-temporal-dropout-prediction-of-e-learning-students/248003
From the abstract:
"This work proposes a dropout prediction approach that is able to self-adjust their outcomes at any moment of a degree program timeline. To that end, a rule-based classification technique was used to identify courses, grade thresholds and other attributes that have a high influence on the dropout behavior. This approach, which is generic so that it can be applied to any distance learning degree program, returns different rules that indicate how the predictions are adjusted along with academic terms. Experiments were carried out using four rule-based classification algorithms: JRip, OneR, PART and Ridor."